Chaperoning the Cancer: The Proteostatic Functions of the Heat Shock Proteins in Cancer
Bibliographic record
Abstract
BACKGROUND: Protein homeostasis (proteostasis) is vital for the survival of cells in physiological and pathological conditions. Particularly, cancer cells are in constant state of cellular stress due to rapid proliferation and decreased quality control in proteosynthesis and therefore, are exceedingly dependent on the homeostasis pathways. Among the complex biological mechanisms regulating proteostasis are the highly conserved molecular chaperones, heat shock proteins (HSPs). HSPs assist cell survival by catalysing the proper folding of proteins, modulation of the apoptotic machinery and finally regulating the protein degradation machinery, providing either the stability or the degradation of selected proteins under stress conditions. Inevitably, HSPs are upregulated in malignancies and participate in different hallmarks of cancer, with indispensable roles in the onset and progression of the disease. Moreover, high levels of HSPs contribute to poor prognosis and treatment resistance in various cancers. Therefore these molecular chaperones present as attractive targets for anti-cancer therapy. OBJECTIVE: This review describes how HSPs regulate different hallmarks of cancer and provides an overview on the most relevant patents which have recently appeared in the literature. METHODS: The patents were extracted from Google Patents (2012-2016) while the clinical trial results were mined from www.clinicaltrial.gov. RESULTS AND CONCLUSION: Review of literature shows that the proteostatic functions of HSPs can modify different hallmarks of cancer. Moreover, targeting HSPs (notably HSP27, HSP70 and HSP90) exhibited positive results in clinical trials so far. However, more studies should be designed to optimize the efficacy of mono or combination therapy in various malignancies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".